I’ve been getting a few hits for the term “productive outs” lately. I blame TBS (so does Steve Goldman). When the stat came into being five years ago, I did a little study of its impact, and I thought I’d repost my findings.

If there is anything of use in POP it must be in addition to the impact of OBP and SLG, not an alternative measure. Olney’s argument ought to be: all else being equal, teams that have a higher percentage of productive outs will score more runs than those that do not. This means that when two teams have identical OPSs the one with a higher POP will score more runs. So, what happens when I run a regression including both OPS and POP, which allows me to control for the run-scoring abilities of teams due to OBP and SLG, to capture any additional POP effect? Well, not much. Using the 2004 team data provided by ESPN.com I find that POP has no effect on run-scoring. Though the coefficient is negative it is not statistically significant.

So, why doesn’t it have an effect? I mean, clearly logic dictates that productive outs are preferred to non-productive outs. The problems lies in the fact that productive out situations are also productive at-bat situations. While productive outs are preferred to non-productive outs, non-outs are even better. A team that is producing productive outs is still producing outs.

Tuesday, October 13th, 2009,
by JC and is filed under "Hitting, Sabermetrics ".
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7 Responses “On Productive Outs”

Just because the coefficient is not statistically significant does not mean it is zero, just that it is small (relative to what could be detected, power, etc.). I think you should not say it’s zero, you should just say that it doesn’t help much with prediction, given what you already have to use in your prediction. In fact, maybe that is what you DID say!

I noticed a few nights ago on TBS that they showed what teams had the most productive outs during the season and I instantly wondered “yeah, but what % of their total plate appearances is that?” …I think that might hold more significance to their run scoring +/- than just lookin’ at productive outs. I could be wrong though.

It means the estimated effect is not outside the the normal variation if the effect is zero. I’m fine with stating the effect is zero. Considering that the coefficient estimates are negative, claiming no effect is actually charitable.

I can’t contribute to the statistical analysis discussion. Obviously, no one goes up looking to make an out. But, clearly, if you are up with a runner on third and less than two outs (assuming the infield is back), it’s better to hit a ground ball to second than to strike out. (The Yankees won the 1962 World Series on a GIDP with the bases loaded.) And, presumably, this is somewhat within the hitter’s control–you could cut down your swing, for example, to increase your chances of striking out.

The problem is, whether this is really a “productive” out depends on the situation. If it’s a tie game in the 9th inning, a sacrifice fly is productive. If it’s the first inning and the bases are loaded, the sac fly actually is helping to kill the rally. “Productive” outs often lead to cheap and meaningless RBIs (another reason why RBIs are often meaningless stats), such as when a team is behind by several runs in the late innings and the hitter drives a runner in from third with a ground ball. It helps the hitter’s RBI numbers but doesn’t really help the team. In a case like this, it really isn’t a productive out (even though Joe Simpson would probably say it is) because it will actually depress the number of runs scored compared to a non-out.

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